Modeling artificial, mobile swarm systems

Using social network analysis to inform disease control interventions

A novel formation tracking control approach for uncertain artificial swarm systems is designed. In this paper, agents can not only track fixed targets but also perform swarm behaviors, and the swarm behaviors are regarded as control targets for the agent. By the Udwadia-Kalaba theory, dynamic control is presented for each agent to ensure that the artificial swarm systems meet the required movement. Additionally, the uncertainties of the agents, which is unknown and time-varying (but bound), are considered. To estimate boundary information, a new adaptive law is designed. The artificial swarm systems' performances under the designed method are guaranteed by Lyapunov stability and numerical verification by simulation.

Fields studying animal movement are data-starved due to large monetary and time costs of data collection, and, moreover, not all quantities of interest can be measured. Despite limited data, diverse models have been developed to study animal movement. Many of these models are based on random walks that use Gaussian noise. Examinations of real movement data show that the assumptions made in these models are not always valid on all spatial and time scales, and these shortcomings suggest that new models may be needed. We provide methods for developing and training more realistic models, specifically agent-based models of animal movement, by taking a data-driven approach. We took an exploratory data analysis (EDA) approach that allowed us to develop a model that best reproduced the movement patterns we observed in a real dataset of animal locations. In our EDA, we examined distributions of positions, calculated the autocorrelation of the movement data, used Fourier analysis, calculated mean-squared displacements, and tested for correlations. We introduce the fused-lasso regression-analysis method for identifying large and sudden changes in position through a non-parametric fit that is sensitive to discontinuities. We introduce the copula and kernel-density estimates as methods for approximating coordinate-system-independent movement correlations from the marginal location differences represented in our data set; use of the copula allows us to create correlated, non-Gaussian noise. With the insights gained from our EDA, we created a Langevin model that describes the movements of an individual animal, features non-Gaussian noise and incorporates multiple movement patterns. We extended this model to an agent-based model that describes the movements of several groups of deer. We compared our Langevin model to three models built on different assumptions that result in substantial differences in the area covered by an animal. Using our agent-based model, we simulated three groups of deer with parameters sampled from our data to illustrate the amount of area covered by deer in our model and the ability of groups to overlap in space. This overlapping behavior is an important feature of models of processes driven by interactions between animals, such as infectious diseases; our model could be useful for studying the spread of infectious diseases in animal populations, such as chronic wasting disease. While we have applied our data-driven methods to animal movement, these methods are widely applicable to developing and training models using a wide range of data sources.

Swarm intelligence, inspired by the collective behavior observed in social organisms, has emerged as a powerful paradigm in both natural and artificial systems. The concept of a swarm refers to a large group of simple agents that interact locally with one another and their environment, giving rise to complex and intelligent behavior at the group level. Swarm intelligence, on the other hand, represents the ability of a swarm to self-organize, adapt, and solve complex problems without central control. In nature, swarms of social insects such as bees, ants, termites, and birds exhibit remarkable abilities in foraging, navigation, resource allocation, and defense. These organisms demonstrate how the interactions of simple individuals can lead to efficient and robust solutions to various challenges faced in their environments. In artificial systems, researchers have successfully translated the principles of swarm intelligence into algorithms and techniques for optimization, decision-making, and problem-solving. Popular swarm intelligence algorithms, such as Ant Colony Optimization, Particle Swarm Optimization, and Artificial Bee Colony, have shown great promise in tackling complex optimization and search tasks. This paper provides an overview of the fundamental concepts of swarm intelligence and explores the similarities and differences between natural and artificial swarms. It delves into the principles of self-organization, decentralized decision-making, and adaptation that underpin swarm intelligence, allowing these systems to cope with dynamic and uncertain environments. Furthermore, the paper examines the application domains of swarm intelligence, ranging from robotics and autonomous systems to data clustering, image processing, and network routing. The potential of swarm robotics in solving real-world challenges, such as environmental monitoring, disaster response, and precision agriculture, is also explored. Swarm intelligence presents a compelling avenue for understanding and harnessing emergent collective behavior in both biological and computational contexts. The interplay of simplicity, local interactions, and adaptation enables swarms to tackle complex problems efficiently, making them a valuable source of inspiration for the design of intelligent systems in various fields. The study of swarm intelligence continues to advance, offering exciting possibilities for creating adaptive, robust, and scalable solutions in the ever-evolving landscape of artificial intelligence and beyond.

Vaccines and other alternative products can help minimize the need for antibiotics by preventing and controlling infectious diseases in animal populations, and are central to the future success of animal agriculture. To assess scientific advancements related to alternatives to antibiotics and provide actionable strategies to support their development, the United States Department of Agriculture, with support from the World Organisation for Animal Health, organized the second International Symposium on Alternatives to Antibiotics. It focused on six key areas: vaccines; microbial-derived products; non-nutritive phytochemicals; immune-related products; chemicals, enzymes, and innovative drugs; and regulatory pathways to enable the development and licensure of alternatives to antibiotics. This article, part of a two-part series, synthesizes and expands on the expert panel discussions regarding opportunities, challenges and needs for the development of vaccines that may reduce the need for use of antibiotics in animals; new approaches and potential solutions will be discussed in part 2 of this series. Vaccines are widely used to prevent infections in food animals. Various studies have demonstrated that their animal agricultural use can lead to significant reductions in antibiotic consumption, making them promising alternatives to antibiotics. To be widely used in food producing animals, vaccines have to be safe, effective, easy to use, and cost-effective. Many current vaccines fall short in one or more of these respects. Scientific advancements may allow many of these limitations to be overcome, but progress is funding-dependent. Research will have to be prioritized to ensure scarce public resources are dedicated to areas of potentially greatest impact first, and private investments into vaccine development constantly compete with other investment opportunities. Although vaccines have the potential to improve animal health, safeguard agricultural productivity, and reduce antibiotic consumption and resulting resistance risks, targeted research and development investments and concerted efforts by all affected are needed to realize that potential.

This chapter is focused on ecoepidemiology. It introduces and studies a number of models related to infectious diseases in animal populations. Animals are typically subject to ecological interactions. The chapter first introduces SI and SIR models of species subject to a generalist predator and studies the impact of selective and indiscriminate predation. The classical Lotka–Volterra predator–prey and competition models are reviewed together with their basic mathematical properties. Furthermore, the chapter includes and discusses a Lotka–Volterra predator–prey model with disease in prey and a Lotka–Volterra competition model with disease in one of the species. Hopf bifurcation and chaos are found in some of the ecoepidemiological models.

BackgroundBiosecurity is at the forefront of the fight against infectious diseases in animal populations. Few research studies have attempted to identify and quantify the effectiveness of biosecurity against disease introduction or presence in cattle farms and, when done, they have relied on the collection of on-farm data. Data on environmental, animal movement, demographic/husbandry systems and density disease determinants can be collated without requiring additional specific on-farm data collection activities, since they have already been collected for some other purposes. The aim of this study was to classify cattle herds according to their risk of disease presence as a proxy for compromised biosecurity in the cattle population of Wales in 2004 for risk-based surveillance purposes.ResultsThree data mining methods have been applied: logistic regression, classification trees and factor analysis. Using the cattle holding population in Wales, a holding was considered positive if at least bovine TB or one of the ten most frequently diagnosed infectious or transmissible non-notifiable diseases in England and Wales, according to the Veterinary Investigation Surveillance Report (VIDA) had been diagnosed in 2004. High-risk holdings can be described as open large cattle herds located in high-density cattle areas with frequent movements off to many locations within Wales. Additional risks are associated with the holding being a dairy enterprise and with a large farming area.ConclusionThis work has demonstrated the potential of mining various livestock-relevant databases to obtain generic criteria for individual cattle herd biosecurity risk classification. Despite the data and analytical constraints the described risk profiles are highly specific and present variable sensitivity depending on the model specifications. Risk profiling of farms provides a tool for designing targeted surveillance activities for endemic or emerging diseases, regardless of the prior amount of information available on biosecurity at farm level. As the delivery of practical evidence-based information and advice is one of the priorities of Defra's new Animal Health and Welfare Strategy (AHWS), data-driven models, derived from existing databases, need to be developed that can then be used to inform activities during outbreaks of endemic diseases and to help design surveillance activities.

The main goal of this study was to investigate the occurrence of porcine reproductive and respiratory syndrome virus (PRRSV)-specific genotypes in swine sites in Ontario (Canada) using molecular, spatial and network data from a porcine reproductive and respiratory syndrome (PRRS) regional control project. For each site, location, animal movement service provider (truck companies), PRRSV status and sequencing data of the open reading frame 5 (ORF5) were obtained. Three-kilometre buffers were created to evaluate neighbourhood characteristics for each site. Social network analysis was conducted on swine sites and trucking companies to assemble the network and define network components. Three different PRRSV genotypes were used as outcomes for statistical analysis based on the region's phylogenetic tree of the ORF5. Multivariable exact logistic regression was conducted to investigate the association between being positive for a specific genotype and two main exposures of interest: (i) having at least one neighbour within threekm also positive for the same genotype outside the production system and (ii) having at least one positive site for the same genotype in the same truck network component outside the production system. Results showed that the importance of area spread and truck network on PRRSV occurrence differed according to genotype. Additionally, the Ontario PRRS database appears suitable for conducting regional disease investigations. Finally, the use of relatively new tools available for network, spatial and molecular analysis could be useful in investigation, control and prevention of endemic infectious diseases in animal populations.

Understanding pathogen transmission is essential to addressing the dynamics of infectious diseases in animal populations. Directly transmitted parasites spread in host populations via 1) contact with infected individuals and 2) contact with contaminated substrates. Although studies exist that support social or ranging effects on transmission, it is less clear how these factors interact. We test the hypothesis that a combination of social, ranging, diet, and intrinsic factors account for Trypanoxyuris minutus (pinworm) infections in sympatric howler species Alouatta palliata and A. pigra. We collected 211 howler fecal samples from 34 adults living in four groups, two of each species, in Tabasco (Mexico), and calculated pinworm prevalence and eggs per gram of feces (EPG). We followed each group for 80 h to determine ranging, diet, frequency of contact, and conspecific proximity. Prevalence of Trypanoxyurisminutus was high, with 82% of all individuals infected. Logistic modeling indicated that pinworm prevalence was positively associated with proximity and the proportion of group members contacted by focal individuals. Although EPG results should be interpreted cautiously owing to variable egg excretion, this index was also positively associated with proximity and the proportion of group members that were contacted, as well as with dietary diversity and use of non-tree foods. Neither intrinsic factors such as species and sex, nor group and population level variables, such as group and home range size, home range overlap, and intensity of range use, were significant predictors of pinworm infection. We conclude that both sociality and feeding behavior are key factors in infection dynamics of Trypanoxyuris minutus in sympatric Alouatta palliata and A. pigra, confirming that contact with infected conspecifics and contaminated substrates are important mechanisms for directly transmitted parasites.

Vaccines and other alternative products are central to the future success of animal agriculture because they can help minimize the need for antibiotics by preventing and controlling infectious diseases in animal populations. To assess scientific advancements related to alternatives to antibiotics and provide actionable strategies to support their development, the United States Department of Agriculture, with support from the World Organisation for Animal Health, organized the second International Symposium on Alternatives to Antibiotics. It focused on six key areas: vaccines; microbial-derived products; non-nutritive phytochemicals; immune-related products; chemicals, enzymes, and innovative drugs; and regulatory pathways to enable the development and licensure of alternatives to antibiotics. This article, the second part in a two-part series, highlights new approaches and potential solutions for the development of vaccines as alternatives to antibiotics in food producing animals; opportunities, challenges and needs for the development of such vaccines are discussed in the first part of this series. As discussed in part 1 of this manuscript, many current vaccines fall short of ideal vaccines in one or more respects. Promising breakthroughs to overcome these limitations include new biotechnology techniques, new oral vaccine approaches, novel adjuvants, new delivery strategies based on bacterial spores, and live recombinant vectors; they also include new vaccination strategies in-ovo, and strategies that simultaneously protect against multiple pathogens. However, translating this research into commercial vaccines that effectively reduce the need for antibiotics will require close collaboration among stakeholders, for instance through public–private partnerships. Targeted research and development investments and concerted efforts by all affected are needed to realize the potential of vaccines to improve animal health, safeguard agricultural productivity, and reduce antibiotic consumption and resulting resistance risks.

Deforestation has become a significant environmental factor that influences the spread of many infectious diseases worldwide. With the degradation or fragmentation of natural habitats, the ecological balance is disrupted, leading to increased interactions among humans, wild animals, and disease vectors. The paper explores the role of deforestation in fueling the transmission of ecologically mediated, zoonotic, and environmentally mediated infectious diseases due to ecological disruption, loss of biodiversity, and disruption in the reservoirs of the pathogens. Recent epidemiological and environmental research evidence shows that there are high rates of association between forest clearing and increased rates of diseases like malaria, dengue, Lyme disease, Ebola, and other zoonoses. Furthermore, the study highlights the role of habitat destruction on wildlife populations, particularly how it increases the risk of pathogen spillover from animals to humans, with wildlife acting as key reservoirs for infectious agents. These effects are usually mediated by changes in vector populations, host availability, and increased human contact with hitherto isolated infectious agents. Moreover, deforestation increases climate variability, which also affects vector breeding, pathogen survival, and the intensity of spillage. These effects on the environment have far-reaching health impacts on the population. The burden of disease places additional pressure on already weak health systems, especially in low- and middle-income areas where forest cover is lowest. Mitigation needs to be implemented effectively through an integrated approach that combines sustainable land-use planning, enhanced surveillance, ecological conservation, and community-based public health. The article highlights the need for interdisciplinary collaboration among environmental science, epidemiology, and policymaking to minimize disease risks associated with forest loss. Finally, it is essential to save forest ecosystems not only to ensure a sustainable environment but also to protect the health of the world's people.

Simulation analysis of the effect of herd immunity and age structure on infection of a cattle herd with bluetongue viruses in Queensland, Australia

A plethora of studies on self-organization has been carried out in broad areas including chemistry, biology, astronomy, medical science, telecommunications, etc., in both academia and industry. Following the studies on swarm intelligence observed in social species, the artificial self-organized systems are expected to exhibit some intelligent features (e.g., flexibility, robustness, decentralized control, self-evolution, etc.) that may have made social species so successful in the biosphere. In this paper, the application of swarm intelligence in communications networks will be studied, and we survey different aspects of bio-inspired mechanisms and examine various algorithms that have been proposed to improve the performance of artificial systems. Some fundamental self-organized networking (SON) mechanisms, designing principles and optimization approaches for artificial systems will then be investigated, followed by some well-known bio-inspired algorithms (e.g., cooperation, division of labor, distributed network synchronization, load balancing, etc.) as well as their applications to the maintenance/operation/optimization of artificial systems being analyzed. Besides, some new emerging technologies, such as the Self-X capabilities and cognitive machine-to-machine (M2M) optimization for the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE)/LTE-Advanced systems, are also surveyed. Finally, the remaining challenges to be faced in designing the future heterogeneous systems will be discussed.

After seven chapters of swarm-based approaches, where do we stand? First of all, it is clear that social insects and, more generally, natural systems, can bring much insight into the design of algorithms and artificial problem-solving systems. In particular, artificial swarm-intelligent systems are expected to exhibit the features that may have made social insects so successful in the biosphere: flexibility, robustness, decentralized control, and self-organization. The examples that have been described throughout this book provide illustrations of these features, either explicitly or implicitly. The swarm-based approach, therefore, looks promising, in face of a world that continually becomes more complex, dynamic, and overloaded with information than ever. There remain some issues, however, as to the application of swarm intelligence to solving problems. . . . 1. First, it would be very useful to define methodologies to “program” a swarm or multiagent system so that it performs a given task. There is a similarity here with the problem of training neural networks [167]: how can one tune interaction weights so that the network performs a given task, such as classification, recognition, etc. The fact that (potentially mobile) agents in a swarm can take actions asynchronously and at any spatial location generally makes the problem extremely hard. In order to solve this “inverse” problem and find the appropriate individual algorithm that generates the desired collective pattern, one can either systematically explore the behaviors of billions of different swarms, or search this huge space of possible swarms with some kind of cost function, assuming a reasonable continuity of the mapping from individual algorithms to collective productions. This latter solution can be based, for example, on artificial evolutionary techniques such as genetic algorithms [152, 171] if individual behavior is adequately coded and if a cost function can be defined. 2. Second, and perhaps even more fundamental than the issue of programming the system, is that of defining it: How complex should individual agents be? Should they be all identical? Should they have the ability to learn? Should they be able to make logical inferences? Should they be purely reactive? How local should their knowledge of the environment be?

Swarm intelligence refers to the phenomenon of a system of spatially distributed individuals that coordinate their actions in a decentralized and self-organized manner, so as to exhibit complex collective behavior. Such systems tend to have large numbers of individual agents that interact with each other in simple ways. This allows swarm-intelligent systems to be inherently robust and flexible. As these principles are scale-free, systems with these properties can range in size from the nano to the macro scale. Swarm-intelligent systems are common throughout nature. Examples are bacteria colonies, neural networks, social insects, and flocks/herds of vertebrates. In addition, humans have produced a variety of (artificial) swarm systems ranging from swarm-based optimization algorithms to sensor networks, swarms of robots, and smart materials. In each of these natural or artificial systems, populations of agents change their spatiotemporal configuration solely based on the agents’ local interactions with each other and the environment. This special issue on ‘‘Swarm Robotics’’ provides an overview of recent results and trends in this emerging field. Contributions to this special issue range from programming paradigms for swarming systems to specific distributed algorithms and modular robotic systems. The unifying theme of these works is individual simplicity: complex global behavior emerges from purely local interactions and simple local rules. Examples covered in this special issue range from spatial behaviors such as flocking and dispersion, computational behaviors such as shortest-path routing and collective decisions, up to full-body behaviors of modular robot ensembles. In their paper, ‘‘Composable continuous-space programs for robotic swarms’’, Bachrach, Beal, and McLurkin present the functional programming language Proto that allows individual behavior to be described by expressions over a global field. By computing the global field not only from local measurements but also based on data received from other swarm members within the local neighborhood, previous state, and control logic, Proto allows complex swarming behaviors to be composed with highly compact code. Proto code is then compiled into op-codes for the Proto Virtual Machine, which needs to provide abstractions for sensing, actuation, estimation of the geometric relations between neighboring swarm members, and local communication. Algorithms such as shortest-path routing are demonstrated on a swarm of 40 miniature mobile robots, as well as in computer simulations. In their paper, ‘‘Collective decision-making based on social odometry’’, Gutierrez, Campo, Monasterio-Huelin, Magdalena, and Dorigo investigate a novel collective decision-making mechanism using a colony of mobile robots that accomplish a foraging task. The robots are required to establish a path from a central place to the closest of multiple resource sites. To reach a consensus, they make use of social odometry. The latter mechanism enables the robots to estimate the position of resource sites by exchanging and aggregating odometry-based positional information and confidence levels. The collective decisionmaking mechanism is successfully validated by experiment N. Correll (&) Department of Computer Science, University of Colorado at Boulder, 430 UCB, Boulder, CO 80309, USA e-mail: nikolaus.correll@colorado.edu